1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
|
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""TensorFlow Ops for loss computation."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.framework import deprecated
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops as array_ops_
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops.losses import losses
@deprecated('2016-12-01', 'Use `tf.losses.mean_squared_error` '
'and explicit logits computation.')
def mean_squared_error_regressor(tensor_in, labels, weights, biases, name=None):
"""Returns prediction and loss for mean squared error regression."""
with ops.name_scope(name, 'mean_squared_error_regressor',
[tensor_in, labels]):
predictions = nn.xw_plus_b(tensor_in, weights, biases)
if len(labels.get_shape()) == 1 and len(predictions.get_shape()) == 2:
predictions = array_ops_.squeeze(predictions, squeeze_dims=[1])
return predictions, losses.mean_squared_error(labels, predictions)
@deprecated('2016-12-01', 'Use `tf.losses.softmax_cross_entropy` '
'and explicit logits computation.')
def softmax_classifier(tensor_in,
labels,
weights,
biases,
class_weight=None,
name=None):
"""Returns prediction and loss for softmax classifier.
This function returns "probabilities" and a cross entropy loss. To obtain
predictions, use `tf.argmax` on the returned probabilities.
This function requires labels to be passed in one-hot encoding.
Args:
tensor_in: Input tensor, [batch_size, feature_size], features.
labels: Tensor, [batch_size, n_classes], one-hot labels of the output
classes.
weights: Tensor, [batch_size, feature_size], linear transformation
matrix.
biases: Tensor, [batch_size], biases.
class_weight: Tensor, optional, [n_classes], weight for each class.
If not given, all classes are supposed to have weight one.
name: Operation name.
Returns:
`tuple` of softmax predictions and loss `Tensor`s.
"""
with ops.name_scope(name, 'softmax_classifier', [tensor_in, labels]):
logits = nn.xw_plus_b(tensor_in, weights, biases)
if class_weight is not None:
logits = math_ops.multiply(logits, class_weight)
return nn.softmax(logits), losses.softmax_cross_entropy(labels, logits)
|
